deepboost.gridSearch: Returns optimised parameter list for deepboost model on given...

In deepboost: Deep Boosting Ensemble Modeling

Description

Returns optimised parameter list for deepboost model on given data

Usage

deepboost.gridSearch(formula, data, k = 10, seed = 666, logging_level = 1)

Arguments

formula	A R Formula object see : ?formula
data	input data.frame as training for model
k	number of folds (default = 10) for cross validation optimisation
seed	for random split to train / test (default 666)
logging_level	print extra data while training 0 - no data, 1 - gridSearch data (default), 2 - all data

Details

Finds optimised parameters for deepboost training. using grid search techniques over: - predefined, battle tested parameter possible values - cross validation over k folds

Value

vector with average accuracy for chosen parameters, and a list of the best parameter combination: (accuracy, (num_iter, beta, lambda, loss_type))

Examples

deepboost.gridSearch(y ~ .,
 data.frame(x1=rep(c(0,0,1,1),2),x2=rep(c(0,1,0,1),2),y=factor(rep(c(0,0,0,1),2))), k=2)

Example output

[1] "average accuracy : 0.744047619047619"
[1] "accuracy: 0.75, num_iter: 5, beta: 1, lambda: 1e-04, loss_type: l"
[[1]]
[1] 0.7440476

[[2]]
[1] 5

[[3]]
[1] 1e-04

[[4]]
[1] 1

[[5]]
[1] "l"

deepboost documentation built on May 2, 2019, 8:35 a.m.